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## VideoCraftXtend: AI-Enhanced Text-to-Video Generation with Extended Length and Enhanced Motion Smoothness
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<a href='https://huggingface.co/spaces/ychenhq/VideoCrafterXtend'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
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------
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## Introduction
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VideoCraftXtend is an open-source video generation and editing toolbox for crafting video content.
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This project aims to tackle challenges in T2V generation, specifically focusing on the production of long videos, enhancing motion smoothness quality and improving content diversity. We propose a comprehensive framework that integrates a T2V diffusion model, utilizes the OpenAI GPT API, incorporates a Video Quality Assessment (VQA) model, and refines an Interpolation model.
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### 1. Generic Text-to-video Generation
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Click the GIF to access the high-resolution video.
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<table class="center">
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<td>
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<video width="320" controls>
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<source src="https://github.com/chloeleehn/VideoCraftXtend/blob/main/VideoCrafter/results/cat/0001.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</td>
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<td>
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<video width="320" controls>
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<source src="https://github.com/chloeleehn/VideoCraftXtend/blob/main/VideoCrafter/results/cat/0002.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</td>
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<td>
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<video width="320" controls>
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<source src="https://github.com/chloeleehn/VideoCraftXtend/blob/main/VideoCrafter/results/cat/0003.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</td>
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<tr>
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<td style="text-align:center;" width="320">"There is a cat dancing on the sand."</td>
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<td style="text-align:center;" width="320">"Behold the mesmerizing sight of a cat elegantly dancing amidst the soft grains of sand."</td>
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<td style="text-align:center;" width="320">"The fluffy cat is joyfully prancing and twirling on the soft golden sand, its elegant movements mirroring the peaceful seaside setting."</td>
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<tr>
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</table >
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## βοΈ Setup
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### 1. Install Environment
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1) Via Anaconda
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```bash
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conda create -n videocraftxtend python=3.8.5
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conda activate videocraftxtend
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pip install -r requirements.txt
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```
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2) Using Google Colab Pro
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### 2. Download the model checkpoints
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1) Download pretrained T2V models via [Hugging Face](https://huggingface.co/VideoCrafter/VideoCrafter2/blob/main/model.ckpt), and put the `model.ckpt` in `VideoCrafter/checkpoints/base_512_v2/model.ckpt`.
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2) Download pretrained Interpolation models viea [Google Drive](https://drive.google.com/drive/folders/1TBEwF2PmSGyDngP1anjNswlIfwGh2NzU?usp=sharing), and put the `flownet.pkl` in `VideoCrafter/ECCV2022-RIFE/train_log/flownet.pkl`.
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## π« Inference
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### 1. Text-to-Video local Gradio demo
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1) Open `VideoCraftXtend.ipynb`, run the cells till generating Gradio Interface.
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2) Input prompt, customize the parameters and get the resulting video
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3) The last section of the file is evaluation results been put in our report)
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4) Open the `VideoCraftXtend.ipynb` notebook and run the cells until you reach the point where the Gradio interface is generated.
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5) Once the Gradio interface is generated, you can input prompts and customize the parameters according to your requirements. The resulting video should be generated within an estimated timeframe of 15-20 minutes.
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6) The last section of `VideoCraftXtend.ipynb` contains the evaluation results that were included in our report.
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---
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4) [VBench](https://github.com/Vchitect/VBench)
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5) [RIFE](https://github.com/hzwer/ECCV2022-RIFE)
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Thanks the authors for sharing their codebases!
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## π’ Disclaimer
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We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes.
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****
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---
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title: VideoCrafterXen
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emoji: π
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colorFrom: gray
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colorTo: yellow
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sdk: gradio
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sdk_version: 4.27.0
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app_file: app.py
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pinned: false
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---
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